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COURSE TOPIC

The Data Science Workflow: From Question to Evidence

Follow a reproducible data science process from a clear question and data checks to analysis, evaluation, and communication.

Part of course Data Science & Machine Learning Course
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Difficulty Level
Beginner
Estimated Learning Time
60-90 minutes
OVERVIEW

About The Data Science Workflow: From Question to Evidence

A data science project starts with a question, not a model. Define what decision or understanding is needed, identify what data can answer it, and record assumptions before analysis begins.

LEARNING OUTCOMES

What You Will Learn

  • Turn a broad request into an answerable data question.
  • Inspect data quality and document preparation choices.
  • Communicate findings, uncertainty, and limitations clearly.
TOPIC GUIDE

Learn The Data Science Workflow: From Question to Evidence

Frame the question

Clarify the audience, decision, unit of analysis, time period, and evidence needed. A measurable question reduces the temptation to search for patterns without knowing what they mean.

Understand the data

Record where data came from, how it was collected, what each row represents, and which fields may be missing or inconsistent. Use public or synthetic data for practice and avoid personal records without proper authorization.

Prepare and explore

Check types, duplicates, missingness, ranges, and category definitions. Make cleaning decisions explicit. Use descriptive statistics and visualizations to inspect patterns, but distinguish a visible association from a causal explanation.

Model only when useful

A model is one possible tool, not the goal of every project. Compare against a simple baseline, choose metrics that fit the question, and validate with data that was not used to fit the model.

Communicate and reproduce

Summarize the result, uncertainty, assumptions, and limits. Keep code and steps organized so another person can reproduce the analysis and understand what evidence supports each conclusion.

PRACTICAL EXAMPLE

Example / Code

Example 
Question: Which service categories changed most over time?
Unit: One synthetic service request
Checks: Dates, duplicates, missing category labels
Analysis: Monthly counts by category
Caveat: Counts do not explain why a change occurred
TRY IT YOURSELF

Practice Task

Apply what you learned with a practical activity.

Choose a small public or synthetic dataset. Write one answerable question, describe what a row means, list three data-quality checks, and draft a result statement that separates an observed pattern from a possible explanation.

TOOLKIT

Tools & Technologies

  • Python
  • Jupyter
  • Pandas
  • Git
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COURSE TOPICS

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Python, NumPy, and Pandas for Data Science

Use Python arrays and Pandas tables to inspect, clean, transform, and summarize data with documented choices.

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Statistics and Visualization for Machine Learning

Use descriptive statistics and charts to understand distributions, sampling, uncertainty, and relationships before modeling.

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Supervised Machine Learning: Regression and Classification

Understand labeled data, features and targets, simple baselines, and the difference between regression and classification.

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Evaluate Machine Learning Models Without Data Leakage

Choose metrics that match the task and avoid leakage, overfitting, and misleading evaluation splits.

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Data Science Capstone: A Reproducible Project from Start to Finish

Plan a compact data science project with documented sources, repeatable preparation, a baseline, evaluation, and clear limitations.

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Go Beyond The Data Science Workflow: From Question to Evidence

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